Abstract:The rapid adoption of low-precision arithmetic in artificial intelligence and edge computing has created a strong demand for energy-efficient and flexible floating-point multiply-accumulate (MAC) units. This paper presents a fully pipelined dual-precision floating-point MAC processing engine supporting FP8 formats (E4M3, E5M2) and FP4 formats (E2M1, E1M2), specifically optimized for low-power and high-throughput AI workloads. The proposed architecture employs a novel bit-partitioning technique that enables a single 4-bit unit multiplier to operate either as a standard 4x4 multiplier for FP8 or as two parallel 2x2 multipliers for 2-bit operands, achieving 100 percent hardware utilization without duplicating logic. Implemented in 28 nm technology, the proposed processing engine achieves an operating frequency of 1.94 GHz with an area of 0.00396 mm^2 and power consumption of 2.13 mW, resulting in up to 60.4 percent area reduction and 86.6 percent power savings compared to state-of-the-art designs.
Abstract:This work presents Bio-RV, a compact and resource-efficient RISC-V processor intended for biomedical control applications, such as accelerator-based biomedical SoCs and implantable pacemaker systems. The proposed Bio-RV is a multi-cycle RV32I core that provides explicit execution control and external instruction loading with capabilities that enable controlled firmware deployment, ASIC bring-up, and post-silicon testing. In addition to coordinating accelerator configuration and data transmission in heterogeneous systems, Bio-RV is designed to function as a lightweight host controller, handling interfaces with pacing, sensing, electrogram (EGM), telemetry, and battery management modules. With 708 LUTs and 235 flip-flops on FPGA prototypes, Bio-RV, implemented in a 180 nm CMOS technology, operate at 50 MHz and feature a compact hardware footprint. According to post-layout results, the proposed architectural decisions align with minimal energy use. Ultimately, Bio-RV prioritises deterministic execution, minimal hardware complexity, and integration flexibility over peak computing speed to meet the demands of ultra-low-power, safety-critical biomedical systems.